Context Engineering Over Model Size for AI Agents

The Shift from Prompting to Context Engineering

Agentic failures in production are rarely due to model intelligence but rather to broken context. While prompt engineering focuses on static instructions and schema enforcement, context engineering manages the runtime environment that determines what state an agent sees at each step. Production-ready context must be navigable, fresh, fast, and compounding. Most systems fail because they treat context as transient, wiping it at the end of every session and forcing the model to relearn conventions or environment configurations.

Implementing Persistent Memory with Redis

To solve the compounding problem, agents require a two-tier memory architecture: working memory for active task scratchpads and long-term memory for durable facts. By integrating a Redis-based Agent Memory Server via the Model Context Protocol (MCP), developers can enable cross-session persistence. The system automatically parses conversations to extract durable facts, storing them as hashes with associated vector embeddings. This allows the agent to perform semantic retrieval rather than keyword-based lookups, ensuring that project-specific rules (such as dependency management preferences or linting tools) are applied consistently across new sessions without manual re-prompting.

Mechanical Rules for Context

To ensure reliability, context pipelines must adhere to four specific constraints:

• Navigability: Agents must traverse relational paths between entities rather than performing blind lookups over raw text.
• Freshness: Data must be real-time to prevent the agent from acting confidently on stale information.
• Latency: Querying must be performant, as sequential round trips across multiple APIs compound into unusable response times.
• Compounding: The system must automatically extract and store durable facts from user interactions so the agent improves over time.